Today, the use of hands-free equipment in mobile telephones and other communications devices is increasing. A well known problem associated with hands-free solutions, particularly in automobile applications, is that of disruptive background noise being picked up at a hands-free microphone and transmitted to a far-end user. In other words, since the distance between a hands-free microphone and a near-end user can be relatively large, the hands-free microphone picks up not only the near-end user's speech, but also any noise which happens to be present at the near-end location. For example, in an automobile telephone application, the near-end microphone typically picks up surrounding traffic, road and passenger compartment noise. The resulting noisy near-end speech can be annoying or even intolerable for the far-end user. It is thus desirable that the background noise be reduced as much as possible, preferably early in the near-end signal processing chain (e.g., before the received near-end microphone signal is input to a near-end speech coder).
As a result, many hands-free systems include a noise reduction processor designed to eliminate background noise at the input of a near-end signal processing chain. FIG. 1 is a high-level block diagram of such a hands-free system 100. In FIG. 1, a noise reduction processor 110 is positioned at the output of a hands-free microphone 120 and at the input of a near-end signal processing path (not shown). In operation, the noise reduction processor 110 receives a noisy speech signal x from the microphone 120 and processes the noisy speech signal x to provide a cleaner, noise-reduced speech signal S.sub.NR which is passed through the near-end signal processing chain and ultimately to the far-end user.
One well known method for implementing the noise reduction processor 110 of FIG. 1 is referred to in the art as spectral subtraction. See, for example, S. F. Boll, Suppression of Acoustic Noise in Speech using Spectral Subtraction, IEEE Trans. Acoust. Speech and Sig. Proc., 27:113-120, 1979, which is incorporated herein by reference. Generally, spectral subtraction uses estimates of the noise spectrum and the noisy speech spectrum to form a signal-to-noise (SNR) based gain function which is multiplied with the input spectrum to suppress frequencies having a low SNR. Though spectral subtraction does provide significant noise reduction, it suffers from several well known disadvantages. For example, the spectral subtraction output signal typically contains artifacts known in the art as musical tones. Further, discontinuities between processed signal blocks often lead to diminished speech quality from the far-end user perspective.
Many enhancements to the basic spectral subtraction method have been developed in recent years. See, for example, N. Virage, Speech Enhancement Based on Masking Properties of the Auditory System, IEEE ICASSP. Proc. 796-799 vol. 1, 1995; D. Tsoukalas, M. Paraskevas and J. Mourjopoulos, Speech Enhancement using Psychoacoustic Criteria, IEEE ICASSP. Proc., 359-362 vol. 2, 1993; F. Xie and D. Van Compernolle, Speech Enhancement by Spectral Magnitude Estimation--A Unifying Approach, IEEE Speech Communication, 89-104 vol. 19, 1996; R. Martin, Spectral Subtraction Based on Minimum Statistics, UESIPCO, Proc., 1182-1185 vol. 2, 1994; and S. M. McOlash, R. J. Niederjohn and J. A. Heinen, A Spectral Subtraction Method for Enhancement of Speech Corrupted by Nonwhite, Nonstationary Noise, IEEE IECON. Proc., 872-877 vol. 2, 1995.
While these methods do provide varying degrees of speech enhancement, it would nonetheless be advantageous if alternative techniques for addressing the above described spectral subtraction problems relating to musical tones and inter-block discontinuities could be developed. Consequently, there is a need for improved methods and apparatus for performing noise reduction by spectral subtraction.